Pen-enabled devices such as tablet pc's and personal digital assistants often use one or more types of handwriting recognizers to allow users to enter data using the pen. Handwriting recognizers analyze the user's handwriting according to a series of rules to determine the most likely match. Typically, the ink segments are compared to ink samples in a database to determine a list of probabilities of the most likely results. Most recognizers also use language models and/or dictionaries to further improve recognition results.
One way to assure that words that do not follow the rules of the language model or are not in the dictionary can still be recognizer is to use two different modules—in-dictionary module and out-of-dictionary module—each producing their own list of best matching results. The merging of these lists into one list of alternates presents a significant difficulty.